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authorandreas128 <Andreas>2017-09-13 18:34:05 +0200
committerandreas128 <Andreas>2017-09-13 18:34:05 +0200
commit426671f4af2a5d1d5d01225194eb66b2edb8c059 (patch)
tree994a98618521afcec5e6d19c443ba85eaa8af51e
parenta6ab5661fabc51aa1bdfc48c44bdef4b1f30e095 (diff)
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Fix non closed figures
-rw-r--r--dpd/src/Agc.py2
-rw-r--r--dpd/src/Dab_Util.py10
-rw-r--r--dpd/src/ExtractStatistic.py2
-rw-r--r--dpd/src/MER.py2
-rw-r--r--dpd/src/Model.py4
-rw-r--r--dpd/src/Model_AM.py2
-rw-r--r--dpd/src/phase_align.py2
-rwxr-xr-xdpd/src/subsample_align.py2
8 files changed, 13 insertions, 13 deletions
diff --git a/dpd/src/Agc.py b/dpd/src/Agc.py
index 978b607..b83c91e 100644
--- a/dpd/src/Agc.py
+++ b/dpd/src/Agc.py
@@ -139,7 +139,7 @@ class Agc:
fig.tight_layout()
fig.savefig(fig_path)
- fig.clf()
+ plt.close(fig)
# The MIT License (MIT)
diff --git a/dpd/src/Dab_Util.py b/dpd/src/Dab_Util.py
index e3dbfe3..37be5db 100644
--- a/dpd/src/Dab_Util.py
+++ b/dpd/src/Dab_Util.py
@@ -49,7 +49,7 @@ class Dab_Util:
plt.plot(c, label="corr")
plt.legend()
plt.savefig(corr_path)
- plt.clf()
+ plt.close()
return np.argmax(c) - off + 1
@@ -118,7 +118,7 @@ class Dab_Util:
fig.tight_layout()
fig.savefig(fig_path)
- fig.clf()
+ plt.close(fig)
off_meas = self.lag_upsampling(sig_rx, sig_tx, n_up=1)
off = int(abs(off_meas))
@@ -161,7 +161,7 @@ class Dab_Util:
fig.tight_layout()
fig.savefig(fig_path)
- fig.clf()
+ plt.close(fig)
sig_rx = sa.subsample_align(sig_rx, sig_tx)
@@ -185,7 +185,7 @@ class Dab_Util:
fig.tight_layout()
fig.savefig(fig_path)
- fig.clf()
+ plt.close(fig)
sig_rx = pa.phase_align(sig_rx, sig_tx)
@@ -209,7 +209,7 @@ class Dab_Util:
fig.tight_layout()
fig.savefig(fig_path)
- fig.clf()
+ plt.close(fig)
logging.debug("Sig1_cut: %d %s, Sig2_cut: %d %s, off: %d" % (len(sig_tx), sig_tx.dtype, len(sig_rx), sig_rx.dtype, off))
return sig_tx, sig_rx
diff --git a/dpd/src/ExtractStatistic.py b/dpd/src/ExtractStatistic.py
index d2aeee4..6139e1d 100644
--- a/dpd/src/ExtractStatistic.py
+++ b/dpd/src/ExtractStatistic.py
@@ -118,7 +118,7 @@ class ExtractStatistic:
fig.tight_layout()
fig.savefig(fig_path)
- fig.clf()
+ plt.close(fig)
pickle.dump(self.rx_values_lists, open("/tmp/rx_values", "wb"))
pickle.dump(self.tx_values, open("/tmp/tx_values", "wb"))
diff --git a/dpd/src/MER.py b/dpd/src/MER.py
index 00fcc23..4f2918e 100644
--- a/dpd/src/MER.py
+++ b/dpd/src/MER.py
@@ -106,7 +106,7 @@ class MER:
plt.tight_layout()
plt.savefig(fig_path)
plt.show()
- plt.clf()
+ plt.close()
MER_res = 20 * np.log10(np.mean([10 ** (MER / 20) for MER in MERs]))
return MER_res
diff --git a/dpd/src/Model.py b/dpd/src/Model.py
index a051927..fe9c56b 100644
--- a/dpd/src/Model.py
+++ b/dpd/src/Model.py
@@ -139,7 +139,7 @@ class Model:
)
plt.hist(phase_diff_choice)
plt.savefig('/tmp/hist_' + str(np.random.randint(0, 1000)) + '.svg')
- plt.clf()
+ plt.close()
phase_diff_est, phase_A = self.dpd_phase(rx_choice)
err_phase = phase_diff_est - phase_diff_choice
self.errs_pm.append(np.mean(np.abs(err_phase ** 2)))
@@ -326,7 +326,7 @@ class Model:
fig.tight_layout()
fig.savefig(fig_path)
- fig.clf()
+ plt.close(fig)
self.coefs_am = new_coefs_am
self.coefs_am_history.append(self.coefs_am)
diff --git a/dpd/src/Model_AM.py b/dpd/src/Model_AM.py
index 5281dde..bdf55c6 100644
--- a/dpd/src/Model_AM.py
+++ b/dpd/src/Model_AM.py
@@ -71,7 +71,7 @@ class Model_AM:
fig.tight_layout()
fig.savefig(fig_path)
- fig.clf()
+ plt.close(fig)
def poly(self, sig):
return np.array([sig ** i for i in range(1, 6)]).T
diff --git a/dpd/src/phase_align.py b/dpd/src/phase_align.py
index 7f82392..7317d70 100644
--- a/dpd/src/phase_align.py
+++ b/dpd/src/phase_align.py
@@ -75,7 +75,7 @@ def phase_align(sig, ref_sig, plot=False):
plt.legend(loc=4)
plt.tight_layout()
plt.savefig(fig_path)
- plt.clf()
+ plt.close()
return sig
diff --git a/dpd/src/subsample_align.py b/dpd/src/subsample_align.py
index 6d1cd2a..b0cbe88 100755
--- a/dpd/src/subsample_align.py
+++ b/dpd/src/subsample_align.py
@@ -78,7 +78,7 @@ def subsample_align(sig, ref_sig, plot=False):
plt.plot(ixs, taus)
plt.title("Subsample correlation, minimum is best: {}".format(best_tau))
plt.savefig(tau_path)
- plt.clf()
+ plt.close()
# Prepare rotate_vec = fft_sig with rotated phase
rotate_vec = np.exp(1j * best_tau * omega)